Mount Sinai and other elite hospitals are pouring millions of dollars into chatbots and AI tools, as doctors and nurses worry the technology will upend their jobs.
Mount Sinai has become a laboratory for AI, trying to shape the future of medicine. But some healthcare workers fear the technology comes at a cost.
AI can be an amazing tool in healthcare, as a double check. For example, assume a doctor thinks you have something. Right now you could have
A simple non invasive diagnostic procedure that costs X that is 90% accurate.
A complex invasive diagnostic procedure that costs 10X that is 98% accurate.
Doctor will always suggest the first one and then see if you need the second one based on other factors. AI can be a great tool as a double checker. You go in, do the simple test, and then you run your results and your inputs through a model and that’ll give you a second probability, and that could help determine that you should go in for the more invasive one.
If it’s done that way it’ll be a great tool, but AI should never be used as the only check or to replace real proven tests. At the end of the day it’s still saying “From my information I’ve trained on, the answer to the question of 2+2 is probably 4”, it does not do any actual calculations. Only probabilities from trained data. So great at double checking, but bad at being the single source.
There was an interview I saw with a cancer researcher working with AI to improve cancer detection in early imaging - they’ve fed thousands of CT and X-ray images to their model, and have then gone back through the data when patients have had biopsies to confirm. This sort of high quality data and attentive follow up has the potential to provide such better screening for cancers and other conditions that patients could have additional months or years to address them.
Doctors will never use a test that is only 90% accurate.
A more realistic scenario is to start with a simple test that has low false negative rate (<5%) but prone to obtain a false positive. If the test is negative then testing stops. If it is positive then they confirm the diagnosis with a more complex test with a low false positive rate.
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AI can be an amazing tool in healthcare, as a double check. For example, assume a doctor thinks you have something. Right now you could have
Doctor will always suggest the first one and then see if you need the second one based on other factors. AI can be a great tool as a double checker. You go in, do the simple test, and then you run your results and your inputs through a model and that’ll give you a second probability, and that could help determine that you should go in for the more invasive one.
If it’s done that way it’ll be a great tool, but AI should never be used as the only check or to replace real proven tests. At the end of the day it’s still saying “From my information I’ve trained on, the answer to the question of 2+2 is probably 4”, it does not do any actual calculations. Only probabilities from trained data. So great at double checking, but bad at being the single source.
Expert systems have been available as part of medical diagnoses for decades. I remember ahem finding one for the Apple ][+ back in the day.
https://pubmed.ncbi.nlm.nih.gov/2663006/ was written back in '89 and you can easily find others going further back.
AI in medical diagnostic capabilities is nothing new or surprising.
There was an interview I saw with a cancer researcher working with AI to improve cancer detection in early imaging - they’ve fed thousands of CT and X-ray images to their model, and have then gone back through the data when patients have had biopsies to confirm. This sort of high quality data and attentive follow up has the potential to provide such better screening for cancers and other conditions that patients could have additional months or years to address them.
Doctors will never use a test that is only 90% accurate.
A more realistic scenario is to start with a simple test that has low false negative rate (<5%) but prone to obtain a false positive. If the test is negative then testing stops. If it is positive then they confirm the diagnosis with a more complex test with a low false positive rate.